Prediction of enteric methane emissions by sheep using an intercontinental database

作者：Alejandro Belanche, A.N. Hristov, Henk J. van Lingen, Stuart E. Denman, E. Kebreab, Angela Schwarm, Michael Kreuzer, Mutian Niu, Maguy Eugène, Vincent Niderkorn, Cécile Martin, Harry Archimède, Mark McGee, C.K. Reynolds, L.A. Crompton, A.R. Bayat, Zhongtang Yu, A. Bannink, J. Dijkstra, Alex V. Chaves, H. Clark, Stefan Muetzel, Vibeke Lind, J. M. Moorby, John Rooke, Aurélie Aubry, W. Antezana, Min Wang, R. S. Hegarty, V. H. Oddy, J. Hill, Philip E. Vercoe, Jean Víctor Savian, Adibe Luiz Abdalla, Yosra A. Soltan, Alda Lúcia Gomes Monteiro, Juan Carlos Kú-Vera, G. Jaurena, Carlos Gómez, Olga Lucía Mayorga, Guilhermo Francklin de Souza Congio, David R. Yáñez-Ruíz · 发表于：Journal of Cleaner Production · 年份：2022 · DOI：10.1016/j.jclepro.2022.135523 · 被引用次数：35 · 研究领域：Ruminant Nutrition and Digestive Physiology、Agriculture Sustainability and Environmental Impact、Genetic and phenotypic traits in livestock

Enteric methane (CH4) emissions from sheep contribute to global greenhouse gas emissions from livestock. However, as already available for dairy and beef cattle, empirical models are needed to predict CH4 emissions from sheep for accounting purposes. The objectives of this study were to: 1) collate an intercontinental database of enteric CH4 emissions from individual sheep; 2) identify the key variables for predicting enteric sheep CH4 absolute production (g/d per animal) and yield [g/kg dry matter intake (DMI)] and their respective relationships; and 3) develop and cross-validate global equations as well as the potential need for age-, diet-, or climatic region-specific equations. The refined intercontinental database included 2,135 individual animal data from 13 countries. Linear CH4 prediction models were developed by incrementally adding variables. A universal CH4 production equation using only DMI led to a root mean square prediction error (RMSPE, % of observed mean) of 25.4% and an RMSPE-standard deviation ratio (RSR) of 0.69. Universal equations that, in addition to DMI, also included body weight (DMI + BW), and organic matter digestibility (DMI + OMD + BW) improved the prediction performance further (RSR, 0.62 and 0.60), whereas diet composition variables had negligible effects. These universal equations had lower prediction error than the extant IPCC 2019 equations. Developing age-specific models for adult sheep (>1-year-old) including DMI alone (RSR = 0.66) or in co...